"""
Advanced Model Accuracy Enhancement System
State-of-the-art techniques for maximizing prediction accuracy
"""

import numpy as np
import pandas as pd
from typing import Dict, List, Tuple, Optional, Any, Callable, Union
import warnings
warnings.filterwarnings('ignore')

from sklearn.model_selection import TimeSeriesSplit, cross_val_score
from sklearn.ensemble import VotingRegressor, StackingRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
from sklearn.preprocessing import StandardScaler, RobustScaler
from sklearn.feature_selection import SelectKBest, f_regression, RFE, RFECV
from sklearn.decomposition import PCA, FastICA
import optuna
from scipy import stats

try:
    import xgboost as xgb
    import lightgbm as lgb
    import catboost as cb
    BOOSTING_LIBS_AVAILABLE = True
except ImportError:
    BOOSTING_LIBS_AVAILABLE = False

try:
    from sklearn.neural_network import MLPRegressor
    from tensorflow.keras.models import Sequential, Model
    from tensorflow.keras.layers import Dense, LSTM, GRU, Attention, Dropout, BatchNormalization
    from tensorflow.keras.optimizers import Adam, RMSprop
    from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau
    import tensorflow as tf
    NEURAL_LIBS_AVAILABLE = True
except ImportError:
    NEURAL_LIBS_AVAILABLE = False

try:
    from skopt import gp_minimize
    from skopt.space import Real, Integer, Categorical
    SKOPT_AVAILABLE = True
except ImportError:
    SKOPT_AVAILABLE = False

class AdvancedEnsembleOptimizer:
    """
    Advanced ensemble optimization with dynamic weighting and meta-learning
    """
    
    def __init__(self, target_accuracy: float = 0.99):
        self.target_accuracy = target_accuracy
        self.base_models = {}
        self.meta_models = {}
        self.ensemble_weights = {}
        self.performance_history = []
        
        # Model quality thresholds
        self.min_model_accuracy = 0.70
        self.diversity_threshold = 0.3
        
    def optimize_ensemble(self, 
                         models: Dict[str, Any],
                         X_train: np.ndarray,
                         y_train: np.ndarray,
                         X_val: np.ndarray,
                         y_val: np.ndarray) -> Dict[str, Any]:
        """
        Optimize ensemble using advanced techniques
        """
        
        print("🎯 Optimizing ensemble for maximum accuracy...")
        
        # Step 1: Individual model optimization
        optimized_models = self._optimize_individual_models(
            models, X_train, y_train, X_val, y_val
        )
        
        # Step 2: Model selection based on performance and diversity
        selected_models = self._select_diverse_models(
            optimized_models, X_train, y_train, X_val, y_val
        )
        
        # Step 3: Dynamic weight optimization
        optimal_weights = self._optimize_ensemble_weights(
            selected_models, X_train, y_train, X_val, y_val
        )
        
        # Step 4: Meta-model stacking
        meta_model = self._train_meta_model(
            selected_models, X_train, y_train, X_val, y_val
        )
        
        # Step 5: Adaptive ensemble construction
        final_ensemble = self._build_adaptive_ensemble(
            selected_models, optimal_weights, meta_model
        )
        
        # Evaluate final performance
        final_accuracy = self._evaluate_ensemble(
            final_ensemble, X_val, y_val
        )
        
        print(f"✅ Final ensemble accuracy: {final_accuracy:.4f}")
        
        return {
            'ensemble': final_ensemble,
            'accuracy': final_accuracy,
            'weights': optimal_weights,
            'meta_model': meta_model,
            'selected_models': selected_models
        }
    
    def _optimize_individual_models(self, 
                                   models: Dict[str, Any],
                                   X_train: np.ndarray,
                                   y_train: np.ndarray,
                                   X_val: np.ndarray,
                                   y_val: np.ndarray) -> Dict[str, Any]:
        """Optimize each model individually using hyperparameter tuning"""
        
        optimized_models = {}
        
        for model_name, model in models.items():
            print(f"🔧 Optimizing {model_name}...")
            
            try:
                # Define hyperparameter search space based on model type
                if 'xgb' in model_name.lower():
                    optimized_model = self._optimize_xgboost(model, X_train, y_train, X_val, y_val)
                elif 'lgb' in model_name.lower() or 'lightgbm' in model_name.lower():
                    optimized_model = self._optimize_lightgbm(model, X_train, y_train, X_val, y_val)
                elif 'catboost' in model_name.lower():
                    optimized_model = self._optimize_catboost(model, X_train, y_train, X_val, y_val)
                elif 'neural' in model_name.lower() or 'mlp' in model_name.lower():
                    optimized_model = self._optimize_neural_network(model, X_train, y_train, X_val, y_val)
                else:
                    # Generic sklearn model optimization
                    optimized_model = self._optimize_sklearn_model(model, X_train, y_train, X_val, y_val)
                
                # Validate model performance
                val_pred = optimized_model.predict(X_val)
                accuracy = self._calculate_directional_accuracy(y_val, val_pred)
                
                if accuracy >= self.min_model_accuracy:
                    optimized_models[model_name] = optimized_model
                    print(f"✅ {model_name} accuracy: {accuracy:.4f}")
                else:
                    print(f"❌ {model_name} below threshold: {accuracy:.4f}")
                    
            except Exception as e:
                print(f"❌ Failed to optimize {model_name}: {e}")
        
        return optimized_models
    
    def _optimize_xgboost(self, model, X_train, y_train, X_val, y_val):
        """Optimize XGBoost hyperparameters"""
        
        def objective(trial):
            params = {
                'n_estimators': trial.suggest_int('n_estimators', 100, 2000),
                'max_depth': trial.suggest_int('max_depth', 3, 12),
                'learning_rate': trial.suggest_float('learning_rate', 0.01, 0.3),
                'subsample': trial.suggest_float('subsample', 0.7, 1.0),
                'colsample_bytree': trial.suggest_float('colsample_bytree', 0.7, 1.0),
                'reg_alpha': trial.suggest_float('reg_alpha', 1e-8, 10.0, log=True),
                'reg_lambda': trial.suggest_float('reg_lambda', 1e-8, 10.0, log=True),
            }
            
            xgb_model = xgb.XGBRegressor(**params, random_state=42)
            xgb_model.fit(X_train, y_train)
            
            pred = xgb_model.predict(X_val)
            accuracy = self._calculate_directional_accuracy(y_val, pred)
            
            return -accuracy  # Minimize negative accuracy
        
        study = optuna.create_study(direction='minimize')
        study.optimize(objective, n_trials=100, show_progress_bar=False)
        
        best_params = study.best_params
        optimized_model = xgb.XGBRegressor(**best_params, random_state=42)
        optimized_model.fit(X_train, y_train)
        
        return optimized_model
    
    def _optimize_lightgbm(self, model, X_train, y_train, X_val, y_val):
        """Optimize LightGBM hyperparameters"""
        
        def objective(trial):
            params = {
                'n_estimators': trial.suggest_int('n_estimators', 100, 2000),
                'max_depth': trial.suggest_int('max_depth', 3, 12),
                'learning_rate': trial.suggest_float('learning_rate', 0.01, 0.3),
                'subsample': trial.suggest_float('subsample', 0.7, 1.0),
                'colsample_bytree': trial.suggest_float('colsample_bytree', 0.7, 1.0),
                'reg_alpha': trial.suggest_float('reg_alpha', 1e-8, 10.0, log=True),
                'reg_lambda': trial.suggest_float('reg_lambda', 1e-8, 10.0, log=True),
                'num_leaves': trial.suggest_int('num_leaves', 10, 300),
            }
            
            lgb_model = lgb.LGBMRegressor(**params, random_state=42, verbosity=-1)
            lgb_model.fit(X_train, y_train)
            
            pred = lgb_model.predict(X_val)
            accuracy = self._calculate_directional_accuracy(y_val, pred)
            
            return -accuracy
        
        study = optuna.create_study(direction='minimize')
        study.optimize(objective, n_trials=100, show_progress_bar=False)
        
        best_params = study.best_params
        optimized_model = lgb.LGBMRegressor(**best_params, random_state=42, verbosity=-1)
        optimized_model.fit(X_train, y_train)
        
        return optimized_model
    
    def _optimize_catboost(self, model, X_train, y_train, X_val, y_val):
        """Optimize CatBoost hyperparameters"""
        
        def objective(trial):
            params = {
                'iterations': trial.suggest_int('iterations', 100, 2000),
                'depth': trial.suggest_int('depth', 3, 10),
                'learning_rate': trial.suggest_float('learning_rate', 0.01, 0.3),
                'l2_leaf_reg': trial.suggest_float('l2_leaf_reg', 1e-8, 10.0, log=True),
                'border_count': trial.suggest_int('border_count', 32, 255),
                'bagging_temperature': trial.suggest_float('bagging_temperature', 0, 10),
            }
            
            cb_model = cb.CatBoostRegressor(**params, random_state=42, verbose=False)
            cb_model.fit(X_train, y_train)
            
            pred = cb_model.predict(X_val)
            accuracy = self._calculate_directional_accuracy(y_val, pred)
            
            return -accuracy
        
        study = optuna.create_study(direction='minimize')
        study.optimize(objective, n_trials=50, show_progress_bar=False)
        
        best_params = study.best_params
        optimized_model = cb.CatBoostRegressor(**best_params, random_state=42, verbose=False)
        optimized_model.fit(X_train, y_train)
        
        return optimized_model
    
    def _optimize_neural_network(self, model, X_train, y_train, X_val, y_val):
        """Optimize neural network architecture and hyperparameters"""
        
        def objective(trial):
            # Architecture parameters
            n_layers = trial.suggest_int('n_layers', 1, 5)
            hidden_sizes = []
            
            for i in range(n_layers):
                hidden_sizes.append(
                    trial.suggest_int(f'hidden_size_{i}', 32, 512)
                )
            
            # Training parameters
            learning_rate = trial.suggest_float('learning_rate', 1e-5, 1e-1, log=True)
            batch_size = trial.suggest_categorical('batch_size', [16, 32, 64, 128])
            dropout_rate = trial.suggest_float('dropout_rate', 0.1, 0.5)
            
            # Build model
            mlp_model = MLPRegressor(
                hidden_layer_sizes=tuple(hidden_sizes),
                learning_rate_init=learning_rate,
                alpha=trial.suggest_float('alpha', 1e-5, 1e-1, log=True),
                max_iter=1000,
                early_stopping=True,
                validation_fraction=0.1,
                random_state=42
            )
            
            mlp_model.fit(X_train, y_train)
            pred = mlp_model.predict(X_val)
            accuracy = self._calculate_directional_accuracy(y_val, pred)
            
            return -accuracy
        
        study = optuna.create_study(direction='minimize')
        study.optimize(objective, n_trials=50, show_progress_bar=False)
        
        best_params = study.best_params
        
        # Extract architecture parameters
        n_layers = best_params['n_layers']
        hidden_sizes = [best_params[f'hidden_size_{i}'] for i in range(n_layers)]
        
        optimized_model = MLPRegressor(
            hidden_layer_sizes=tuple(hidden_sizes),
            learning_rate_init=best_params['learning_rate'],
            alpha=best_params['alpha'],
            max_iter=1000,
            early_stopping=True,
            validation_fraction=0.1,
            random_state=42
        )
        
        optimized_model.fit(X_train, y_train)
        return optimized_model
    
    def _optimize_sklearn_model(self, model, X_train, y_train, X_val, y_val):
        """Generic sklearn model optimization"""
        # For now, return the original model
        # This can be extended with hyperparameter tuning for specific sklearn models
        model.fit(X_train, y_train)
        return model
    
    def _select_diverse_models(self, 
                              models: Dict[str, Any],
                              X_train: np.ndarray,
                              y_train: np.ndarray,
                              X_val: np.ndarray,
                              y_val: np.ndarray) -> Dict[str, Any]:
        """Select diverse models for ensemble"""
        
        if len(models) <= 3:
            return models  # Keep all if we have few models
        
        # Calculate prediction correlations
        predictions = {}
        for name, model in models.items():
            predictions[name] = model.predict(X_val)
        
        # Build correlation matrix
        pred_df = pd.DataFrame(predictions)
        corr_matrix = pred_df.corr()
        
        # Select diverse models using greedy algorithm
        selected_models = {}
        remaining_models = list(models.keys())
        
        # Start with the best performing model
        accuracies = {}
        for name, model in models.items():
            pred = model.predict(X_val)
            accuracies[name] = self._calculate_directional_accuracy(y_val, pred)
        
        best_model = max(accuracies, key=accuracies.get)
        selected_models[best_model] = models[best_model]
        remaining_models.remove(best_model)
        
        # Greedily add models that are diverse and accurate
        while len(selected_models) < min(10, len(models)) and remaining_models:
            best_candidate = None
            best_score = -np.inf
            
            for candidate in remaining_models:
                # Calculate diversity score
                diversity_scores = []
                for selected_model in selected_models:
                    corr = abs(corr_matrix.loc[candidate, selected_model])
                    diversity_scores.append(1 - corr)  # Higher is more diverse
                
                avg_diversity = np.mean(diversity_scores)
                accuracy = accuracies[candidate]
                
                # Combined score: accuracy + diversity
                score = 0.7 * accuracy + 0.3 * avg_diversity
                
                if score > best_score:
                    best_score = score
                    best_candidate = candidate
            
            if best_candidate and best_score > 0:
                selected_models[best_candidate] = models[best_candidate]
                remaining_models.remove(best_candidate)
            else:
                break
        
        print(f"🎯 Selected {len(selected_models)} diverse models")
        return selected_models
    
    def _optimize_ensemble_weights(self, 
                                  models: Dict[str, Any],
                                  X_train: np.ndarray,
                                  y_train: np.ndarray,
                                  X_val: np.ndarray,
                                  y_val: np.ndarray) -> Dict[str, float]:
        """Optimize ensemble weights using optimization algorithms"""
        
        # Get predictions from all models
        train_predictions = np.array([
            model.predict(X_train) for model in models.values()
        ]).T
        
        val_predictions = np.array([
            model.predict(X_val) for model in models.values()
        ]).T
        
        # Optimize weights using scipy.optimize
        from scipy.optimize import minimize
        
        def objective(weights):
            weights = np.abs(weights)  # Ensure positive weights
            weights = weights / np.sum(weights)  # Normalize
            
            ensemble_pred = np.dot(val_predictions, weights)
            accuracy = self._calculate_directional_accuracy(y_val, ensemble_pred)
            
            return -accuracy  # Minimize negative accuracy
        
        # Initial weights (equal weighting)
        initial_weights = np.ones(len(models)) / len(models)
        
        # Optimize
        result = minimize(
            objective,
            initial_weights,
            method='SLSQP',
            bounds=[(0, 1) for _ in range(len(models))],
            constraints={'type': 'eq', 'fun': lambda w: np.sum(np.abs(w)) - 1}
        )
        
        optimal_weights = np.abs(result.x)
        optimal_weights = optimal_weights / np.sum(optimal_weights)
        
        # Convert to dictionary
        weight_dict = {}
        for i, (model_name, _) in enumerate(models.items()):
            weight_dict[model_name] = optimal_weights[i]
        
        return weight_dict
    
    def _train_meta_model(self, 
                         models: Dict[str, Any],
                         X_train: np.ndarray,
                         y_train: np.ndarray,
                         X_val: np.ndarray,
                         y_val: np.ndarray):
        """Train meta-model for stacking"""
        
        # Generate meta-features using cross-validation
        tscv = TimeSeriesSplit(n_splits=5)
        meta_features = np.zeros((len(X_train), len(models)))
        
        for fold, (train_idx, val_idx) in enumerate(tscv.split(X_train)):
            X_fold_train, X_fold_val = X_train[train_idx], X_train[val_idx]
            y_fold_train, y_fold_val = y_train[train_idx], y_train[val_idx]
            
            for i, (_, model) in enumerate(models.items()):
                # Train model on fold
                fold_model = type(model)(**model.get_params() if hasattr(model, 'get_params') else {})
                fold_model.fit(X_fold_train, y_fold_train)
                
                # Predict on validation fold
                fold_pred = fold_model.predict(X_fold_val)
                meta_features[val_idx, i] = fold_pred
        
        # Train meta-model
        meta_model = xgb.XGBRegressor(
            n_estimators=100,
            max_depth=3,
            learning_rate=0.1,
            random_state=42
        ) if BOOSTING_LIBS_AVAILABLE else MLPRegressor(
            hidden_layer_sizes=(50, 25),
            max_iter=500,
            random_state=42
        )
        
        meta_model.fit(meta_features, y_train)
        
        return meta_model
    
    def _build_adaptive_ensemble(self, 
                                models: Dict[str, Any],
                                weights: Dict[str, float],
                                meta_model):
        """Build adaptive ensemble with multiple prediction strategies"""
        
        class AdaptiveEnsemble:
            def __init__(self, models, weights, meta_model):
                self.models = models
                self.weights = weights
                self.meta_model = meta_model
                
            def predict(self, X):
                # Get predictions from all models
                predictions = np.array([
                    model.predict(X) for model in self.models.values()
                ]).T
                
                # Weighted ensemble prediction
                weighted_pred = np.dot(predictions, list(self.weights.values()))
                
                # Meta-model prediction
                meta_pred = self.meta_model.predict(predictions)
                
                # Adaptive combination (70% weighted, 30% meta)
                final_pred = 0.7 * weighted_pred + 0.3 * meta_pred
                
                return final_pred
        
        return AdaptiveEnsemble(models, weights, meta_model)
    
    def _evaluate_ensemble(self, ensemble, X_val, y_val) -> float:
        """Evaluate ensemble accuracy"""
        pred = ensemble.predict(X_val)
        return self._calculate_directional_accuracy(y_val, pred)
    
    def _calculate_directional_accuracy(self, y_true, y_pred) -> float:
        """Calculate directional accuracy for stock prediction"""
        if len(y_true) <= 1:
            return 0.0
        
        # Calculate returns (price changes)
        true_returns = np.diff(y_true)
        pred_returns = np.diff(y_pred)
        
        # Calculate directional accuracy
        correct_direction = np.sign(true_returns) == np.sign(pred_returns)
        accuracy = np.mean(correct_direction)
        
        return accuracy

class AdvancedFeatureOptimizer:
    """
    Advanced feature optimization and selection
    """
    
    def __init__(self):
        self.selected_features = []
        self.feature_importance = {}
        self.transformation_pipeline = []
        
    def optimize_features(self, 
                         X_train: pd.DataFrame,
                         y_train: pd.Series,
                         X_val: pd.DataFrame,
                         y_val: pd.Series) -> Tuple[pd.DataFrame, pd.DataFrame]:
        """Optimize features for maximum predictive power"""
        
        print("🔧 Optimizing features for maximum accuracy...")
        
        # Step 1: Remove highly correlated features
        X_train_cleaned, X_val_cleaned = self._remove_correlated_features(
            X_train, X_val, threshold=0.95
        )
        
        # Step 2: Feature selection using multiple methods
        X_train_selected, X_val_selected = self._advanced_feature_selection(
            X_train_cleaned, y_train, X_val_cleaned, y_val
        )
        
        # Step 3: Feature transformation and engineering
        X_train_transformed, X_val_transformed = self._transform_features(
            X_train_selected, X_val_selected, y_train
        )
        
        # Step 4: Final feature validation
        final_features = self._validate_features(
            X_train_transformed, y_train, X_val_transformed, y_val
        )
        
        print(f"✅ Optimized features: {X_train_transformed.shape[1]} -> {len(final_features)}")
        
        return X_train_transformed[final_features], X_val_transformed[final_features]
    
    def _remove_correlated_features(self, X_train, X_val, threshold=0.95):
        """Remove highly correlated features"""
        
        # Calculate correlation matrix
        corr_matrix = X_train.corr().abs()
        
        # Find pairs of highly correlated features
        upper_tri = corr_matrix.where(
            np.triu(np.ones(corr_matrix.shape), k=1).astype(bool)
        )
        
        # Select features to drop
        to_drop = [column for column in upper_tri.columns if any(upper_tri[column] > threshold)]
        
        print(f"🔧 Removing {len(to_drop)} highly correlated features")
        
        return X_train.drop(columns=to_drop), X_val.drop(columns=to_drop)
    
    def _advanced_feature_selection(self, X_train, y_train, X_val, y_val):
        """Advanced feature selection using multiple methods"""
        
        # Method 1: Statistical selection (F-test)
        selector_f = SelectKBest(f_regression, k=min(100, X_train.shape[1]))
        X_train_f = selector_f.fit_transform(X_train, y_train)
        selected_features_f = X_train.columns[selector_f.get_support()].tolist()
        
        # Method 2: Recursive Feature Elimination with XGBoost
        if BOOSTING_LIBS_AVAILABLE:
            estimator = xgb.XGBRegressor(n_estimators=50, random_state=42)
            selector_rfe = RFECV(estimator, step=1, cv=3, scoring='neg_mean_squared_error')
            selector_rfe.fit(X_train, y_train)
            selected_features_rfe = X_train.columns[selector_rfe.support_].tolist()
        else:
            selected_features_rfe = selected_features_f
        
        # Method 3: Feature importance from gradient boosting
        if BOOSTING_LIBS_AVAILABLE:
            lgb_model = lgb.LGBMRegressor(n_estimators=100, random_state=42, verbosity=-1)
            lgb_model.fit(X_train, y_train)
            
            feature_importance = pd.DataFrame({
                'feature': X_train.columns,
                'importance': lgb_model.feature_importances_
            }).sort_values('importance', ascending=False)
            
            # Select top features by importance
            top_features = feature_importance.head(min(80, len(feature_importance)))['feature'].tolist()
        else:
            top_features = selected_features_f
        
        # Combine feature selections (intersection for high confidence)
        final_features = list(set(selected_features_f) & set(selected_features_rfe) & set(top_features))
        
        # If intersection is too small, use union of top features
        if len(final_features) < 20:
            final_features = list(set(selected_features_f) | set(top_features))[:50]
        
        print(f"🎯 Selected {len(final_features)} features from {X_train.shape[1]}")
        
        return X_train[final_features], X_val[final_features]
    
    def _transform_features(self, X_train, X_val, y_train):
        """Transform features for better model performance"""
        
        # Robust scaling (less sensitive to outliers)
        scaler = RobustScaler()
        X_train_scaled = pd.DataFrame(
            scaler.fit_transform(X_train),
            columns=X_train.columns,
            index=X_train.index
        )
        X_val_scaled = pd.DataFrame(
            scaler.transform(X_val),
            columns=X_val.columns,
            index=X_val.index
        )
        
        # Add polynomial features for important features
        from sklearn.preprocessing import PolynomialFeatures
        
        # Select top 10 most important features for polynomial expansion
        if BOOSTING_LIBS_AVAILABLE:
            temp_model = lgb.LGBMRegressor(n_estimators=50, random_state=42, verbosity=-1)
            temp_model.fit(X_train_scaled, y_train)
            
            importance_df = pd.DataFrame({
                'feature': X_train_scaled.columns,
                'importance': temp_model.feature_importances_
            }).sort_values('importance', ascending=False)
            
            top_10_features = importance_df.head(10)['feature'].tolist()
            
            # Create polynomial features
            poly = PolynomialFeatures(degree=2, interaction_only=True, include_bias=False)
            X_train_poly = poly.fit_transform(X_train_scaled[top_10_features])
            X_val_poly = poly.transform(X_val_scaled[top_10_features])
            
            # Create column names for polynomial features
            poly_feature_names = [f"poly_{i}" for i in range(X_train_poly.shape[1] - len(top_10_features))]
            
            # Add polynomial features to the original dataset
            poly_df_train = pd.DataFrame(
                X_train_poly[:, len(top_10_features):], 
                columns=poly_feature_names,
                index=X_train_scaled.index
            )
            poly_df_val = pd.DataFrame(
                X_val_poly[:, len(top_10_features):], 
                columns=poly_feature_names,
                index=X_val_scaled.index
            )
            
            X_train_final = pd.concat([X_train_scaled, poly_df_train], axis=1)
            X_val_final = pd.concat([X_val_scaled, poly_df_val], axis=1)
        else:
            X_train_final = X_train_scaled
            X_val_final = X_val_scaled
        
        return X_train_final, X_val_final
    
    def _validate_features(self, X_train, y_train, X_val, y_val):
        """Final feature validation to ensure quality"""
        
        # Remove features with near-zero variance
        from sklearn.feature_selection import VarianceThreshold
        
        variance_selector = VarianceThreshold(threshold=0.01)
        variance_selector.fit(X_train)
        
        high_variance_features = X_train.columns[variance_selector.get_support()].tolist()
        
        # Remove features with too many missing values
        missing_threshold = 0.1
        low_missing_features = [
            col for col in high_variance_features 
            if X_train[col].isna().mean() < missing_threshold
        ]
        
        print(f"🔍 Final validation: {len(low_missing_features)} features passed")
        
        return low_missing_features

# Example usage and testing
if __name__ == "__main__":
    print("🎯 Advanced Model Accuracy Enhancement")
    print("=" * 50)
    
    # Generate sample data for testing
    np.random.seed(42)
    n_samples = 1000
    n_features = 50
    
    X = np.random.randn(n_samples, n_features)
    y = X[:, 0] + 0.5 * X[:, 1] + 0.3 * X[:, 2] + np.random.randn(n_samples) * 0.1
    
    # Split data
    split_point = int(0.8 * n_samples)
    X_train, X_val = X[:split_point], X[split_point:]
    y_train, y_val = y[:split_point], y[split_point:]
    
    # Create sample models
    models = {}
    
    if BOOSTING_LIBS_AVAILABLE:
        models['xgboost'] = xgb.XGBRegressor(random_state=42)
        models['lightgbm'] = lgb.LGBMRegressor(random_state=42, verbosity=-1)
        models['catboost'] = cb.CatBoostRegressor(random_state=42, verbose=False)
    
    if NEURAL_LIBS_AVAILABLE:
        models['neural_net'] = MLPRegressor(hidden_layer_sizes=(100, 50), random_state=42)
    
    if not models:
        from sklearn.ensemble import RandomForestRegressor
        from sklearn.linear_model import Ridge
        
        models['random_forest'] = RandomForestRegressor(random_state=42)
        models['ridge'] = Ridge(random_state=42)
    
    # Test ensemble optimization
    if models:
        print(f"📊 Testing with {len(models)} models...")
        
        optimizer = AdvancedEnsembleOptimizer(target_accuracy=0.95)
        
        result = optimizer.optimize_ensemble(
            models, X_train, y_train, X_val, y_val
        )
        
        print(f"✅ Ensemble optimization complete!")
        print(f"🎯 Final accuracy: {result['accuracy']:.4f}")
        print(f"📊 Model weights: {result['weights']}")
    else:
        print("⚠️ No compatible models available for testing")
    
    # Test feature optimization
    print(f"\n🔧 Testing feature optimization...")
    
    X_train_df = pd.DataFrame(X_train, columns=[f'feature_{i}' for i in range(n_features)])
    X_val_df = pd.DataFrame(X_val, columns=[f'feature_{i}' for i in range(n_features)])
    y_train_series = pd.Series(y_train)
    y_val_series = pd.Series(y_val)
    
    feature_optimizer = AdvancedFeatureOptimizer()
    
    X_train_opt, X_val_opt = feature_optimizer.optimize_features(
        X_train_df, y_train_series, X_val_df, y_val_series
    )
    
    print(f"✅ Feature optimization complete!")
    print(f"📊 Features: {X_train_df.shape[1]} -> {X_train_opt.shape[1]}")
    
    print(f"\n🎯 Accuracy enhancement system ready!")